research-article

CT 2.0

Authors:

Tapani ToivonenAuthors Info & Claims

Koli Calling '21: Proceedings of the 21st Koli Calling International Conference on Computing Education Research

Article No.: 3, Pages 1 - 8

https://doi.org/10.1145/3488042.3488053

Published: 18 November 2021 Publication History

Abstract

CT has been the central rallying point for K-12 computing education at least since the early 2010s. Many teachers, school administrators, and policymakers have joined the movement. A consensus has emerged over the conceptual landscape of CT.

Meanwhile, machine learning (ML) has triggered some major changes in many sectors of computing. Children’s lives today are full of ML-driven services—take TikTok’s spot-on recommendations, social media’s automatic tagging of their friends in photos, and targeted personalized advertisement, just to mention a few. Children cannot learn to think about and design ML technology from learning classical programming. ML is poised to upend the CT consensus.

Look at some of the changes ML has already triggered in computing. It has enabled greatly improved speech and image recognition, powerful recommendations on streaming services, autonomous navigation of cars, super-human performance in board and computer games, and even alternative-reality “deepfake” videos. Most advances in topics above are due to hardware evolution to non-traditional, special purpose architectures, new algorithms such as convolutional neural networks (CNN) or generative adversarial networks (GAN), and new objectives and measures of success.

We will show that several key CT concepts, including debugging, problem-solving workflow, correctness, and notional machines, are insufficient for ML and need to be extended. Moreover, ML introduces new concepts including neural networks, curating and training data, and reinforcement learning that are not part of CT at all. All these changes challenge the traditional views related to teaching CT in K–12.

ML is not the only emerging technology appearing in the computing landscape. Quantum computing and biological computing are not far behind. We need to start rethinking how CT must evolve to anticipate and meet these challenges.

References

[1]

Alfred V. Aho. 2011. Ubiquity Symposium: Computation and Computational Thinking. Ubiquity 2011, January (2011).

Digital Library

[2]

Safinah Ali, Blakeley H Payne, Randi Williams, Hae Won Park, and Cynthia Breazeal. 2019. Constructionism, Ethics, and Creativity: Developing Primary and Middle School Artificial Intelligence Education. In International Workshop on Education in Artificial Intelligence K–12 (EDUAI’19). Chicago, IL, USA.

[3]

Valerie Barr and Chris Stephenson. 2011. Bringing Computational Thinking to K-12: What is Involved and What is the Role of the Computer Science Education Community?ACM Inroads 2, 1 (2011), 48–54.

Digital Library

[4]

James Bergstra and Yoshua Bengio. 2012. Random search for hyper-parameter optimization.Journal of machine learning research 13, 2 (2012).

[5]

Erik Brynjolfsson and Andrew McAfee. 2014. The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W. W. Norton & Company, New York, NY, USA.

Digital Library

[6]

Alexander Craik, Yongtian He, and Jose L Contreras-Vidal. 2019. Deep learning for electroencephalogram (EEG) classification tasks: a review. Journal of neural engineering 16, 3 (2019), 031001.

[7]

Adnan Darwiche. 2018. Human-level Intelligence or Animal-like Abilities?Commun. ACM 61, 10 (2018), 56–67.

Digital Library

[8]

Peter J. Denning and Matti Tedre. 2019. Computational Thinking. The MIT Press, Cambridge, MA, USA.

[9]

Stefania Druga, Sarah T. Vu, Eesh Likhith, and Tammy Qiu. 2019. Inclusive AI Literacy for Kids around the World. In Proceedings of FabLearn 2019 (New York, NY, USA) (FL2019). ACM, New York, NY, USA, 104–111. https://doi.org/10.1145/3311890.3311904

Digital Library

[10]

J. Estevez, G. Garate, and M. Graña. 2019. Gentle Introduction to Artificial Intelligence for High-School Students Using Scratch. IEEE Access 7(2019), 179027–179036.

[11]

Ignacio Evangelista, Germán Blesio, and Emanuel Benatti. 2018. Why Are We Not Teaching Machine Learning at High School? A Proposal. In 2018 World Engineering Education Forum - Global Engineering Deans Council (WEEF-GEDC). IEEE, 1–6.

[12]

Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016. Machine learning basics. Deep learning 1(2016), 98–164.

[13]

Ian Goodfellow, Yoshua Bengio, Aaron Courville, and Yoshua Bengio. 2016. Deep learning. Vol. 1. MIT press Cambridge.

[14]

Google. 2017. Teachable Machine (v1). https://teachablemachine.withgoogle.com/v1/

[15]

Alex Graves, Greg Wayne, and Ivo Danihelka. 2014. Neural turing machines. arXiv preprint arXiv:1410.5401(2014).

[16]

M Ugur Gudelek, S Arda Boluk, and A Murat Ozbayoglu. 2017. A deep learning based stock trading model with 2-D CNN trend detection. In 2017 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 1–8.

[17]

Fredrik Heintz and Teemu Roos. 2021. Elements Of AI - Teaching the Basics of AI to Everyone in Sweden. In Proceedings of the 13th International Conference on Education and New Learning Technologies (EDULEARN21). IATED, Online, 2568–2572. https://doi.org/10.21125/edulearn.2021.0559

[18]

Arne Hintz, Karin Wahl-Jorgensen, and Lina Dencik. 2018. Digital Citizenship in a Datafied Society. Polity Press, Cambridge, UK.

[19]

Tom Hitron, Yoav Orlev, Iddo Wald, Ariel Shamir, Hadas Erel, and Oren Zuckerman. 2019. Can Children Understand Machine Learning Concepts? The Effect of Uncovering Black Boxes. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow, Scotland, UK) (CHI ’19). ACM, New York, NY, USA, 1–11. https://doi.org/10.1145/3290605.3300645

Digital Library

[20]

Tom Hitron, Iddo Wald, Hadas Erel, and Oren Zuckerman. 2018. Introducing Children to Machine Learning Concepts through Hands-on Experience. In Proceedings of the 17th ACM Conference on Interaction Design and Children (Trondheim, Norway) (IDC ’18). ACM, New York, NY, USA, 563–568. https://doi.org/10.1145/3202185.3210776

Digital Library

[21]

Joshua W. K. Ho and Matthew Scadding. 2019. Classroom Activities for Teaching Artificial Intelligence to Primary School Students. In Proceedings of International Conference on Computational Thinking Education 2019, S.C. Kong, D. Andone, G. Biswas, H.U. Hoppe, T.C. Hsu, R.H. Huang, B.C. Kuo, K.Y. Li, C.K. Looi, M. Milrad, J. Sheldon, J.L. Shih, K.F. Sin, K.S. Song, and J. Vahrenhold (Eds.). The Education University of Hong Kong, Hong Kong, 157–159.

[22]

Guosheng Hu, Yongxin Yang, Dong Yi, Josef Kittler, William Christmas, Stan Z Li, and Timothy Hospedales. 2015. When face recognition meets with deep learning: an evaluation of convolutional neural networks for face recognition. In Proceedings of the IEEE international conference on computer vision workshops. 142–150.

Digital Library

[23]

Sven Jatzlau, Tilman Michaeli, Stefan Seegerer, and Ralf Romeike. 2019. It’s not Magic After All – Machine Learning in Snap! using Reinforcement Learning. In 2019 IEEE Blocks and Beyond Workshop (B&B). IEEE, Memphis, TN, USA, 37–41.

[24]

Kenneth Kahn, Rani Megasari, Erna Piantari, and Enjun Junaeti. 2018. AI Programming by Children using Snap! Block Programming in a Developing Country. In EC-TEL Practitioner Proceedings 2018: 13th European Conference On Technology Enhanced Learning, Vania Dimitrova, Sambit Praharaj, Mikhail Fominykh, and Hendrik Drachsler (Eds.). Leeds, UK.

[25]

Kenneth Kahn and Niall Winters. 2017. Child-Friendly Programming Interfaces to AI Cloud Services. In Data Driven Approaches in Digital Education, Élise Lavoué, Hendrik Drachsler, Katrien Verbert, Julien Broisin, and Mar Pérez-Sanagustín (Eds.). Springer International Publishing, Cham, 566–570.

[26]

Andrej Karpathy. 2017. Software 2.0. https://medium.com/@karpathy/software-2-0-a64152b37c35

[27]

Mehul M Khimasia and Peter V Coveney. 1997. Protein structure prediction as a hard optimization problem: the genetic algorithm approach. Molecular Simulation 19, 4 (1997), 205–226.

[28]

Dale Lane. 2017. Machine Learning for Kids. https://machinelearningforkids.co.uk/

[29]

Duri Long and Brian Magerko. 2020. What is AI Literacy? Competencies and Design Considerations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI ’20). ACM, New York, NY, USA, 1–16. https://doi.org/10.1145/3313831.3376727

Digital Library

[30]

Romany F Mansour. 2018. Deep-learning-based automatic computer-aided diagnosis system for diabetic retinopathy. Biomedical engineering letters 8, 1 (2018), 41–57.

[31]

Hui Mao, Ming Cheung, and James She. 2017. Deepart: Learning joint representations of visual arts. In Proceedings of the 25th ACM international conference on Multimedia. 1183–1191.

Digital Library

[32]

Radu Mariescu-Istodor and Ilkka Jormanainen. 2019. Machine Learning for High School Students. In Proceedings of the 19th Koli Calling International Conference on Computing Education Research (Koli, Finland) (Koli Calling ’19). Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3364510.3364520

Digital Library

[33]

Lívia S. Marques, Christiane Gresse von Wangenheim, and Jean C. R. Hauck. 2020. Teaching Machine Learning in School: A Systematic Mapping of the State of the Art. Informatics in Education 19, 2 (2020), 283–321. https://doi.org/10.15388/infedu.2020.14

[34]

Larry R Medsker and LC Jain. 2001. Recurrent neural networks. Design and Applications 5 (2001).

[35]

Leonardo Noriega. 2005. Multilayer perceptron tutorial. School of Computing. Staffordshire University (2005).

[36]

Seymour Papert. 1980. Mindstorms: Children, Computers, and Powerful Ideas. Basic Books, New York, NY, USA.

Digital Library

[37]

Mitchel Resnick and Brian Silverman. 2005. Some Reflections on Designing Construction Kits for Kids. In Proceedings of the 2005 Conference on Interaction Design and Children (Boulder, CO, USA) (IDC ’05). ACM, New York, NY, USA, 117–122. https://doi.org/10.1145/1109540.1109556

Digital Library

[38]

Shahbaz Rezaei and Xin Liu. 2019. Deep learning for encrypted traffic classification: An overview. IEEE communications magazine 57, 5 (2019), 76–81.

[39]

Sebastian Risi and Mike Preuss. 2020. From chess and atari to starcraft and beyond: How game AI is driving the world of AI. KI-Künstliche Intelligenz 34, 1 (2020), 7–17.

[40]

Mohammed Saqr, Kwok Ng, Solomon Sunday Oyelere, and Matti Tedre. 2021. People, Ideas, Milestones: A Scientometric Study of Computational Thinking. ACM Transactions on Computing Education 21, 3, Article 20(2021), 17 pages. https://doi.org/10.1145/3445984

Digital Library

[41]

Andrew W Senior, Richard Evans, John Jumper, James Kirkpatrick, Laurent Sifre, Tim Green, Chongli Qin, Augustin Žídek, Alexander WR Nelson, Alex Bridgland, 2020. Improved protein structure prediction using potentials from deep learning. Nature 577, 7792 (2020), 706–710.

[42]

David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, 2017. Mastering chess and shogi by self-play with a general reinforcement learning algorithm. arXiv preprint arXiv:1712.01815(2017).

[43]

Juha Sorva. 2012. Visual Program Simulation in Introductory Programming Education. Ph. D. Dissertation. Aalto University, Finland.

[44]

Juha Sorva. 2013. Notional Machines and Introductory Programming Education. ACM Transactions on Computing Education 13, 2 (2013), 8:1–8:31.

Digital Library

[45]

Matti Tedre, Tapani Toivonen, Juho Kahila, Henriikka Vartiainen, Teemu Valtonen, Ilkka Jormanainen, and Arnold Pears. 2021. Teaching Machine Learning in K–12 Classroom: Pedagogical and Technological Trajectories for Artificial Intelligence Education. IEEE Access 9(2021), 110558–110572. https://doi.org/10.1109/ACCESS.2021.3097962

[46]

Neil C. Thompson, Kristjan Greenewald, Keeheon Lee, and Gabriel F. Manso. 2020. The Computational Limits of Deep Learning. arxiv:2007.05558 [cs.LG]

[47]

David Touretzky, Christina Gardner-McCune, Fred Martin, and Deborah Seehorn. 2019. Envisioning AI for K-12: What Should Every Child Know about AI?. In Proceedings of the 33rd AAAI Conference on Artificial Intelligence, Vol. 1. Association for the Advancement of Artificial Intelligence, 9795–9799.

Digital Library

[48]

Jessica Van Brummelen, Judy Hanwen Shen, and Evan W Patton. 2019. The Popstar, the Poet, and the Grinch: Relating Artificial Intelligence to the Computational Thinking Framework with Block-based Coding. In Proceedings of International Conference on Computational Thinking Education. 160–161.

[49]

Henriikka Vartiainen, Matti Tedre, and Teemu Valtonen. 2020. Learning machine learning with very young children: Who is teaching whom?International Journal of Child-Computer Interaction 25 (2020), 1–11. https://doi.org/10.1016/j.ijcci.2020.100182

Digital Library

[50]

Henriikka Vartiainen, Tapani Toivonen, Ilkka Jormanainen, Juho Kahila, Matti Tedre, and Teemu Valtonen. 2020. Machine learning for middle schoolers: Children as designers of Machine Learning Apps. In Proceedings of the 2020 FIE Conference. Uppsala, Sweden.

Digital Library

[51]

Henriikka Vartiainen, Tapani Toivonen, Ilkka Jormanainen, Juho Kahila, Matti Tedre, and Teemu Valtonen. 2021. Machine Learning for Middle Schoolers: Learning through Data-Driven Design. International Journal of Child-Computer Interaction 29 (2021), 100281.

Digital Library

[52]

Jeannette M. Wing. 2011. Computational Thinking: What and Why. The LinkMarch 6(2011). https://www.cs.cmu.edu/link/research-notebook-computational-thinking-what-and-why

[53]

Stephen Wolfram. 2017. An Elementary Introduction to the Wolfram Language (2nd ed.). Wolfram Media, Champaign, IL, USA.

[54]

Gary K. W. Wong, Xiaojuan Ma, and John Huen. 2019. When Schools Meet Artificial Intelligence in Hong Kong. ACM Inroads 10, 4 (2019), 43–46. https://doi.org/10.1145/3369739

Digital Library

[55]

Xiaofei Zhou, Jessica Van Brummelen, and Phoebe Lin. 2020. Designing AI Learning Experiences for K–12: Emerging Works, Future Opportunities and a Design Framework. arxiv:2009.10228 [cs.CY]

[56]

Abigail Zimmermann-Niefield, R. Benjamin Shapiro, and Shaun Kane. 2019. Sports and Machine Learning: How Young People Can Use Data from Their Own Bodies to Learn about Machine Learning. ACM XRDS 25, 4 (2019), 44–49. https://doi.org/10.1145/3331071

Digital Library

[57]

Abigail Zimmermann-Niefield, Makenna Turner, Bridget Murphy, Shaun K. Kane, and R. Benjamin Shapiro. 2019. Youth Learning Machine Learning through Building Models of Athletic Moves. In Proceedings of the 18th ACM International Conference on Interaction Design and Children(Boise, ID, USA) (IDC ’19). ACM, New York, NY, USA, 121–132. https://doi.org/10.1145/3311927.3323139

Digital Library

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Koli Calling '21: Proceedings of the 21st Koli Calling International Conference on Computing Education Research

November 2021

287 pages

ISBN:9781450384889

DOI:10.1145/3488042

Editors:
Otto Seppälä
Aalto University, Finland
,
Andrew Petersen
University of Toronto Mississauga, Canada

Copyright © 2021 ACM.

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Published: 18 November 2021

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Koli Calling '21: 21st Koli Calling International Conference on Computing Education Research

November 18 - 21, 2021

Joensuu, Finland

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